An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2.

Genome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.

A new framework for host-pathogen interaction research.

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Impact of comorbidities on the serological response to COVID-19 vaccination in Taiwan (preprint)

Background/Aims Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the best policies to control COVID-19 pandemic. The serological response to COVID-19 vaccination in Taiwanese patients with different comorbidities is elusive. Methods Uninfected subjects who received 3 doses of mRNA vaccines (BNT162b2 [Pfizer-BioNTech, BNT] and mRNA-1273 [Moderna]), viral vector-based vaccines (ChAdOx1-S (AZD1222, AZ) or protein subunit vaccines (Medigen COVID-19 vaccine) were prospectively enrolled. The SARS-CoV-2-IgG spike antibody level was determined within three months after the 3rd dose of vaccination. The Charlson Comorbidity Index (CCI) was applied to determine the association between vaccine titers and underlying comorbidities. Results A total of 824 subjects were enrolled in the current study. The proportions of CCI scores of 0-1, 2-3 and >4 were 52.8% (n=435), 31.3% (n=258) and 15.9% (n=131), respectively. The most commonly used vaccination combination was AZ-AZ-Moderna (39.2%), followed by Moderna-Moderna-Moderna (27.8%). The mean vaccination titer was 3.11 log BAU/mL after a median of 48 days after the 3rd dose. Factors associated with potentially effective neutralization capacity included an age ≥60 years (odds ratio [OR]/95% confidence interval [CI], 0.49/0.34–0.72; P <0.001), female gender (OR/CI, 1.78/1.26–2.53; P =0.001), Moderna-based vaccination (compared to AZ-based vaccination; OR/CI, 6.49/3.90–10.82; P <0.001) and a CCI score ≥4 (OR/CI, 0.55/0.35–0.85; P =0.01). There was a decreasing trend in antibody titers with increasing CCI scores (trend P<0.001). Linear regression analysis revealed that AZ-based vaccination (β: 0.341, CI: 0.144, 0.21, P<0.001) and higher CCI scores (β: -0.055, CI: -0.096, -0.014, P=0.009) independently correlated with low IgG spike antibody levels. Conclusions Subjects with more comorbidities had a poor response to 3 doses of COVID-19 vaccination.

Genome-wide Mendelian randomization and single-cell RNA sequencing analyses identify the causal effects of COVID-19 on 41 cytokines.

The elevated levels of inflammatory cytokines have attracted much attention during the treatment of COVID-19 patients. The conclusions of current observational studies are often controversial in terms of the causal effects of COVID-19 on various cytokines because of the confounding factors involving underlying diseases. To resolve this problem, we conducted a Mendelian randomization analysis by integrating the GWAS data of COVID-19 and 41 cytokines. As a result, the levels of 2 cytokines were identified to be promoted by COVID-19 and had unsignificant pleiotropy. In comparison, the levels of 10 cytokines were found to be inhibited and had unsignificant pleiotropy. Among down-regulated cytokines, CCL2, CCL3 and CCL7 were members of CC chemokine family. We then explored the potential molecular mechanism for a significant causal association at a single cell resolution based on single-cell RNA data, and discovered the suppression of CCL3 and the inhibition of CCL3-CCR1 interaction in classical monocytes (CMs) of COVID-19 patients. Our findings may indicate that the capability of COVID-19 in decreasing the chemotaxis of lymphocytes by inhibiting the CCL3-CCR1 interaction in CMs.

Transcriptome and DNA methylome analysis of peripheral blood samples reveals incomplete restoration and transposable element activation after 3-months recovery of COVID-19.

Comprehensive analyses showed that SARS-CoV-2 infection caused COVID-19 and induced strong immune responses and sometimes severe illnesses. However, cellular features of recovered patients and long-term health consequences remain largely unexplored. In this study, we collected peripheral blood samples from nine recovered COVID-19 patients (median age of 36 years old) from Hubei province, China, 3 months after discharge as well as 5 age- and gender-matched healthy controls; and carried out RNA-seq and whole-genome bisulfite sequencing to identify hallmarks of recovered COVID-19 patients. Our analyses showed significant changes both in transcript abundance and DNA methylation of genes and transposable elements (TEs) in recovered COVID-19 patients. We identified 425 upregulated genes, 214 downregulated genes, and 18,516 differentially methylated regions (DMRs) in total. Aberrantly expressed genes and DMRs were found to be associated with immune responses and other related biological processes, implicating prolonged overreaction of the immune system in response to SARS-CoV-2 infection. Notably, a significant amount of TEs was aberrantly activated and their activation was positively correlated with COVID-19 severity. Moreover, differentially methylated TEs may regulate adjacent gene expression as regulatory elements. Those identified transcriptomic and epigenomic signatures define and drive the features of recovered COVID-19 patients, helping determine the risks of long COVID-19, and guiding clinical intervention.

Metabolic modeling of single bronchoalveolar macrophages reveals regulators of hyperinflammation in COVID-19.

SARS-CoV-2 infection induces imbalanced immune response such as hyperinflammation in patients with severe COVID-19. Here, we studied the immunometabolic regulatory mechanisms for the pathogenesis of COVID-19. We depicted the metabolic landscape of immune cells, especially macrophages, from bronchoalveolar lavage fluid of patients with COVID-19 at single-cell level. We found that most metabolic processes were upregulated in macrophages from lungs of patients with mild COVID-19 compared to cells from healthy controls, whereas macrophages from severe COVID-19 showed downregulation of most of the core metabolic pathways including glutamate metabolism, fatty acid oxidation, citrate cycle, and oxidative phosphorylation, and upregulation of a few pathways such as glycolysis. Rewiring cellular metabolism by amino acid supplementation, glycolysis inhibition, or PPARγ stimulation reduces inflammation in macrophages stimulated with SARS-CoV-2. Altogether, this study demonstrates that metabolic imbalance of bronchoalveolar macrophages may contribute to hyperinflammation in patients with severe COVID-19 and provides insights into treating COVID-19 by immunometabolic modulation.

Analysis of Common Respiratory Infected Pathogens in 3100 Children after the Coronavirus Disease 2019 Pandemic.

OBJECTIVE: To investigate the epidemiological features in children after the coronavirus disease 2019 (COVID-19) pandemic. METHODS: This study collected throat swabs and serum samples from hospitalized pediatric patients of Renmin Hospital of Wuhan University, Wuhan, Hubei province, China before and after the COVID-19 pandemic. Respiratory infected pathogens [adenovirus (ADV), influenza virus A/B (Flu A/B), parainfluenza virus 1/2/3 (PIV1/2/3), respiratory syncytial virus (RSV), Mycoplasma pneumoniae (MP), and Chlamydia pneumoniae (CP)] were detected. The pathogens, age, and gender were used to analyze the epidemiological features in children after the COVID-19 pandemic. RESULTS: The pathogen detection rate was significantly higher in females than in males (P<0.05), and the infection of PIV1 and MP was mainly manifested. After the COVID-19 pandemic, PIV1, PIV3, RSV, and MP had statistically different detection rates among the age groups (P<0.05), and was mainly detected in patients aged 0-6 years, 0-3 years, 0-3 years, and 1-6 years, respectively. When comparing before the COVID-19 pandemic, the total detection rate of common respiratory pathogens was lower (P<0.05). Except for the increase in the detection rate of PIV1 and CP, the infection rate of other pathogens had almost decreased. CONCLUSION: The prevention and control measures for the COVID-19 pandemic effectively changed the epidemiological features of common respiratory tract infectious diseases in pediatric children.

A Bibliometric Analysis of the Association Between Compassion Fatigue and Psychological Resilience From 2008 to 2021.

Aims: A negative association between the lower level of psychological resilience (PR) and increased risk of compassion fatigue (CF) and higher Coronavirus disease 2019 (COVID-19) stress has been revealed. However, bibliometric studies have not been performed to comprehensively investigate this topic. This study aimed to identify the status and trends in the CF and PR field from 2008 to 2021 and during the COVID-19 pandemic. Methods: We identified relevant literature from the Web of Science Core Collection® database using "resilience" and "compassion fatigue" on September 30, 2021. All search results were exported in plain text format for collaboration network analysis, reference-based co-citation analysis, analysis of journals, and keywords-based co-occurrence analysis, which were performed using Citespace® 5.8.R1. Results: A total of 388 publications were identified finally, and there has been an increasing trend in the annual number of publications with light fluctuations. The analysis of journals and keywords indicated that nurses and social workers are the main research targets, and their mental problems are the main research topics. The turnover intention of health care providers has been a research focus, particularly during the COVID-19. Conclusion: The results of the present study help us understand the status of the CF and PR field and its recent developments.

Characterization of Immune Response Diversity in Rodents Vaccinated with a Vesicular Stomatitis Virus Vectored COVID-19 Vaccine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as the prime challenge facing public health safety since 2019. Correspondingly, coronavirus disease 2019 (COVID-19) vaccines have been developed and administered worldwide, varying in design strategies, delivery routes, immunogenicity and protective efficacy. Here, a replication-competent vesicular stomatitis virus (VSV) vectored recombinant COVID-19 vaccine was constructed and evaluated in BALB/c mice and Syrian golden hamsters. In BALB/c mice, intramuscular (i.m.) inoculation of recombinant vaccine induced significantly higher humoral immune response than that of the intranasal (i.n.) inoculation group. Analyses of cellular immunity revealed that a Th1-biased cellular immune response was induced in i.n. inoculation group while both Th1 and Th2 T cells were activated in i.m. inoculation group. In golden hamsters, i.n. inoculation of the recombinant vaccine triggered robust humoral immune response and conferred prominent protective efficacy post-SARS-CoV-2 challenge, indicating a better protective immunity in the i.n. inoculation group than that of the i.m. inoculation group. This study provides an effective i.n.-delivered recombinant COVID-19 vaccine candidate and elucidates a route-dependent manner of this vaccine candidate in two most frequently applied small animal models. Moreover, the golden hamster is presented as an economical and convenient small animal model that precisely reflects the immune response and protective efficacy induced by replication-competent COVID-19 vaccine candidates in other SARS-CoV-2 susceptible animals and human beings, especially in the exploration of i.n. immunization.

Transcriptome and DNA methylome analysis of peripheral blood samples reveals incomplete restoration and transposable element activation after 3-month recovery of COVID-19 (preprint)

Comprehensive analyses showed that SARS-CoV-2 infection caused COVID-19 and induced strong immune responses and sometimes severe illnesses. However, cellular features of recovered patients and long-term health consequences remain largely unexplored. In this study, we collected peripheral blood samples from recovered COVID-19 patients (average age of 35.7 years old) from Hubei province, China, 3 months after discharge; and carried out RNA-seq and whole-genome bisulfite sequencing (WGBS) to identify hallmarks of recovered COVID-19 patients. Our analyses showed significant changes both in expression and DNA methylation of genes and transposable elements (TEs) in recovered COVID-19 patients. We identified 639 misregulated genes and 18516 differentially methylated regions (DMRs) in total. Genes with aberrant expression and DMRs were found to be associated with immune responses and other related biological processes, implicating prolonged overreaction of the immune system in response to SARS-CoV-2 infection. Notably, a significant amount of TEs were aberrantly activated and TE activation was positively correlated with COVID-19 severity. Moreover, differentially methylated TEs may regulate adjacent gene expression as regulatory elements. Those identified transcriptomic and epigenomic signatures define and drive the features of recovered COVID-19 patients, helping determine the risks of long COVID-19, and providing guidance for clinical intervention.

Single-Cell Landscape of Lungs Reveals Key Role of Neutrophil-Mediated Immunopathology during Lethal SARS-CoV-2 Infection.

Due to the limitation of human studies with respect to individual difference or the accessibility of fresh tissue samples, how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in pathological complications in lung, the main site of infection, is still incompletely understood. Therefore, physiologically relevant animal models under realistic SARS-CoV-2 infection conditions would be helpful to our understanding of dysregulated inflammation response in lung in the context of targeted therapeutics. Here, we characterized the single-cell landscape in lung and spleen upon SARS-CoV-2 infection in an acute severe disease mouse model that replicates human symptoms, including severe lung pathology and lymphopenia. We showed a reduction of lymphocyte populations and an increase of neutrophils in lung and then demonstrated the key role of neutrophil-mediated lung immunopathology in both mice and humans. Under severe conditions, neutrophils recruited by a chemokine-driven positive feedback produced elevated "fatal signature" proinflammatory genes and pathways related to neutrophil activation or releasing of granular content. In addition, we identified a new Cd177high cluster that is undergoing respiratory burst and Stfahigh cluster cells that may dampen antigen presentation upon infection. We also revealed the devastating effect of overactivated neutrophil by showing the highly enriched neutrophil extracellular traps in lung and a dampened B-cell function in either lung or spleen that may be attributed to arginine consumption by neutrophil. The current study helped our understanding of SARS-CoV-2-induced pneumonia and warranted the concept of neutrophil-targeting therapeutics in COVID-19 treatment. IMPORTANCE We demonstrated the single-cell landscape in lung and spleen upon SARS-CoV-2 infection in an acute severe disease mouse model that replicated human symptoms, including severe lung pathology and lymphopenia. Our comprehensive study revealed the key role of neutrophil-mediated lung immunopathology in SARS-CoV-2-induced severe pneumonia, which not only helped our understanding of COVID-19 but also warranted the concept of neutrophil targeting therapeutics in COVID-19 treatment.

ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding.

MOTIVATION: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available. RESULTS: We present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks.

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

SCovid: single-cell atlases for exposing molecular characteristics of COVID-19 across 10 human tissues.

SCovid (http://bio-annotation.cn/scovid) aims at providing a comprehensive resource of single-cell data for exposing molecular characteristics of coronavirus disease 2019 (COVID-19) across 10 human tissues. COVID-19, an epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been found to be accompanied with multiple-organ failure since its first report in Dec 2019. To reveal tissue-specific molecular characteristics, researches regarding to COVID-19 have been carried out widely, especially at single-cell resolution. However, these researches are still relatively independent and scattered, limiting the comprehensive understanding of the impact of virus on diverse tissues. To this end, we developed a single-cell atlas of COVID-19. Firstly we collected 21 single-cell datasets of COVID-19 across 10 human tissues paired with control datasets. Then we constructed a pipeline for the analysis of these datasets to reveal molecular characteristics of COVID-19 based on manually annotated cell types. The current version of SCovid documents 1 042 227 single cells of 21 single-cell datasets across 10 human tissues, 11 713 stably expressed genes and 3778 significant differentially expressed genes (DEGs). SCovid provides a user-friendly interface for browsing, searching, visualizing and downloading all detailed information.

Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.

Gut Microbiota May Not Be Fully Restored in Recovered COVID-19 Patients After 3-Month Recovery.

Coronavirus disease 2019 (COVID-19) has infected over 124 million people worldwide. In addition to the development of therapeutics and vaccines, the evaluation of the sequelae in recovered patients is also important. Recent studies have indicated that COVID-19 has the ability to infect intestinal tissues and to trigger alterations of the gut microbiota. However, whether these changes in gut microbiota persist into the recovery stage remains largely unknown. Here, we recruited seven healthy Chinese men and seven recovered COVID-19 male patients with an average of 3-months after discharge and analyzed their fecal samples by 16S rRNA sequencing analysis to identify the differences in gut microbiota. Our results suggested that the gut microbiota differed in male recovered patients compared with healthy controls, in which a significant difference in Chao index, Simpson index, and ß-diversity was observed. And the relative abundance of several bacterial species differed clearly between two groups, characterized by enrichment of opportunistic pathogens and insufficiency of some anti-inflammatory bacteria in producing short chain fatty acids. The above findings provide preliminary clues supporting that the imbalanced gut microbiota may not be fully restored in recovered patients, highlighting the importance of continuous monitoring of gut health in people who have recovered from COVID-19.